Development of a Serum Biomarker Assay That Differentiates Tumor-Associated MUC5AC (NPC-1C ANTIGEN) from Normal MUC5AC

A serum ELISA using a monoclonal antibody that detects a MUC5AC-related antigen (NPC-1C antigen) expressed by pancreatic and colorectal cancer was developed. The NPC-1C antibody reacts with specific epitopes expressed by tumor-associated MUC5AC that does not appear on MUC5AC from normal tissues. Based on observations of a highly specific antibody, we tested the ELISA to differentiate serum from healthy blood donors compared to serum from patients with colorectal or pancreatic cancer. Additionally, patient tumor tissue was stained to examine the expression pattern of MUC5AC-related antigen in pancreatic and colorectal cancers. The results indicate the NPC-1C antibody ELISA distinguished serum of cancer patients from normal donors with very good sensitivity and specificity. Most patient's tumor biopsy exhibited NPC-1C antibody reactivity, indicating that tumor-associated MUC5AC antigen from tumor is shed into blood, where it can be detected by the NPC-1C antibody ELISA. This serum test provides a new tool to aid in the diagnosis of these cancers and immune monitoring of cancer treatment regimens

Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification

<div><p>Fungal infections are a global problem imposing considerable disease burden. One of the unmet needs in addressing these infections is rapid, sensitive diagnostics. A promising molecular diagnostic approach is high-resolution melt analysis (HRM). However, there has been little effort in leveraging HRM data for automated, objective identification of fungal species. The purpose of these studies was to assess the utility of distance methods developed for comparison of time series data to classify HRM curves as a means of fungal species identification. Dynamic time warping (DTW), first introduced in the context of speech recognition to identify temporal distortion of similar sounds, is an elastic distance measure that has been successfully applied to a wide range of time series data. Comparison of HRM curves of the rDNA internal transcribed spacer (ITS) region from 51 strains of 18 fungal species using DTW distances allowed accurate classification and clustering of all 51 strains. The utility of DTW distances for species identification was demonstrated by matching HRM curves from 243 previously identified clinical isolates against a database of curves from standard reference strains. The results revealed a number of prior misclassifications, discriminated species that are not resolved by routine phenotypic tests, and accurately identified all 243 test strains. In addition to DTW, several other distance functions, Edit Distance on Real sequence (EDR) and Shape-based Distance (SBD), showed promise. It is concluded that DTW-based distances provide a useful metric for the automated identification of fungi based on HRM curves of the ITS region and that this provides the foundation for a robust and automatable method applicable to the clinical setting.</p></div

Nearest-neighbor clusters formed from EDR and SBD distances.

<p>(A) EDR distances between the average melt profile from each of the 51 strains in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173320#pone.0173320.t001" target="_blank">Table 1</a> were calculated with an epsilon value of 0.28 standard deviations and window size of 10. The dendrogram resulting from nearest neighbor clustering of the distances is shown. (B) The dendrogram resulting from nearest neighbor clustering of SBD distances is shown. Both trees can be cut into 18 groups, each corresponding to a different species.</p

The effect of DTW step patterns and window sizes on melt curve clustering.

<p>(A) DTW distances between all 204 melt curves obtained for the 51 strains in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173320#pone.0173320.t001" target="_blank">Table 1</a> were calculated using each of the possible Rabiner and Juang step patterns and slope combinations (21). Distances were calculated with no window constraint or with the window size varied from 1 to 20. Dots indicate successful nearest-neighbor clustering of all 204 melt curves into 18 species-specific groups. Green dots indicate step pattern and slope combinations successful even in the absence of window constraints. Red dots represent those distances for which clustering was successful only with the indicated window size. (B) The minimum silhouette width (34) as a function of window size for step pattern Type 6b.</p

Strains used in the present study.

<p>Strains used in the present study.</p

Euclidean distance is a poor metric for comparing melt curves.

<p>(A) Melt profiles of eight samples of <i>C</i>. <i>albicans</i> strain SC5314 illustrating the variation inherent in melt curve acquisition. (B) Dendrogram of nearest neighbor clustering results using Euclidean distances. The four melt curves obtained for each strain in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173320#pone.0173320.t001" target="_blank">Table 1</a> were averaged and the Euclidean distances between the averaged curves was clustered.</p

The melt profile of the ITS region is unique to each species.

<p>The negative first derivative (-dF/dt) of the normalized melt curve of the ITS region is shown for a representative strain of each of the species listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173320#pone.0173320.t001" target="_blank">Table 1</a>.</p